New draft: PageRanking People and Blogs interviews

_posts/2026-05-06-pageranking-pb.md

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+---
+layout: post
+title: PageRanking People and Blogs interviews
+latex: true
+---
+
+I recently learned about [PageRank](https://en.wikipedia.org/wiki/PageRank), the
+original Google algorithm for ranking webpages. It works by constructing a
+Markov chain on links between pages and computing the stationary distribution.
+The stationary probability of a given page measures the page's centrality in the
+web, an index of popularity.
+
+I wanted to try implementing the algorithm myself and fiddle with a few Rust
+libraries, so I wrote a [script](https://github.com/maxkapur/pbnx) that runs
+PageRank on interviews from the [People and Blogs](https://peopleandblogs.com/)
+series. P&B interviews are a tidy dataset for the algorithm, because Manu always
+asks interviewees to recommend other blogs, then he uses these recommendations
+to pick subsequent interviewees. This means the graph is well connected despite
+its small size.
+
+I was going to share the results here, but ranking blogs by popularity seems
+against the spirit of the indie web ethos, so you'll have to run the program
+yourself. (But to make it clear I'm not covering anything up, let me acknowledge
+that [my interview](https://manuelmoreale.com/interview/max-kapur) is in a
+61-way tie for last place, with all the other blogs that had no links to them.)
+
+A few observations from this exercise below.<!--more-->
+
+- I computed the stationary distribution explicitly using `ndarray_linalg::eig`
+  because that's one of the libraries I was fiddling with, but for a graph with
+  a large number of nodes, storing the $$O(n^2)$$ Markov array in memory is
+  impractical; you'd be better off just simulating the random jumps instead.
+- A Monte Carlo simulation would yield cleaner code than my solution, too. Most
+  of the bugs I had to fix in my implementation had to do with constructing the
+  Markov matrix, normalizing columns, and handling edge cases like nodes with
+  outdegree zero. If all you're doing is walking the graph one node at a time,
+  you can address the edge cases procedurally in a way that's more obviously
+  correct (e.g. "if a page has no links, jump randomly to any page").
+- The hard work in Google's implementation would surely have been the data
+  engineering. You have to find the "content" part of every page to avoid
+  overrepresseing header and footer links like About and Privacy, but semantic
+  HTML wasn't a thing yet. And it would take a lot of work to normalize URLs to
+  handle synonyms that differ only by a `www.` prefix or trailing slash (not to
+  mention redirects and permalinks).
+
+Like many developers, the LLM era is interfering with my motivation to post
+[brain coded](https://domm.plix.at/perl/2025_10_braincoded_static_image_gallery.html)
+projects like this. That's especially true when, as here, I'm exercising a
+language or concept that I'm still learning—my PageRank implementation is
+neither performant nor educational. But as I told Manu in the P&B interview, I
+use my blog and GitHub more for accountability than publicity: The goal is to
+simply keep myself writing and coding.
+
+Publish post.